arXiv subjects

Shen Sun

Publications and source records attributed to Shen Sun.

3 recordsLinked to original sources

arXiv2026

Thermo-mechanically coupled phase-field fracture model considering elastocaloric effect of shape memory alloy

Modelling fracture behavior of the shape memory alloy (SMA) that interacts with martensitic transformation and the associated elastocaloric effect (eCE) still remains challenging. Herein, a thermo-mechanically coupled phase-filed fracture model considering elastocaloric effect of SMA is proposed to simulate the cracking process coupled with the non-isothermal martensitic transformation and the associated eCE. In the phase-field model, both the thermal strain induced by eCE and the eigen strain induced by the phase transition are considered. An empirical degradation function is adopted to describe the thermal conductivity decreasing with the fracture order parameter. The model is validated with the finite element method and tensile fracture properties of Mn-Cu SMA are simulated. It is found that the martensite variant nucleates at the stress concentration where the crack initiates, and commonly spreads with an angle of 45 degree. The thermal expansion strain caused by the eCE could strengthen the critical load capacity. A large kinetic parameter for phase transition and the large orientation angle could enhance the strength and temperature change of eCE while the deformation capacity is reduced. The phase-field model demonstrates its ability in the thermal-mechanically coupled toughening of SMA. It also provides a possible fracture-resistance strategy by the utilization of eCE for elastocaloric devices.

cond-mat.mtrl-sci↗

arXiv2023

A holistic review on fatigue properties of additively manufactured metals

Additive manufacturing (AM) technology is undergoing rapid development and emerging as an advanced technique that can fabricate complex near-net shaped and light-weight metallic parts with acceptable strength and fatigue performance. A number of studies have indicated that the strength or other mechanical properties of AM metals are comparable or even superior to that of conventionally manufactured metals, but the fatigue performance is still a thorny problem that may hinder the replacement of currently used metallic components by AM counterparts when the cyclic loading and thus fatigue failure dominates. This article reviews the state-of-art published data on the fatigue properties of AM metals, principally including $S$--$N$ data and fatigue crack growth data. The AM techniques utilized to generate samples in this review include powder bed fusion (e.g., EBM, SLM, DMLS) and directed energy deposition (e.g., LENS, WAAM). Further, the fatigue properties of AM metallic materials that involve titanium alloys, aluminum alloys, stainless steel, nickel-based alloys, magnesium alloys, and high entropy alloys, are systematically overviewed. In addition, summary figures or tables for the published data on fatigue properties are presented for the above metals, the AM techniques, and the influencing factors (manufacturing parameters, e.g., built orientation, processing parameter, and post-processing). The effects of build direction, particle, geometry, manufacturing parameters, post-processing, and heat-treatment on fatigue properties, when available, are provided and discussed. The fatigue performance and main factors affecting the fatigue behavior of AM metals are finally compared and critically analyzed, thus potentially providing valuable guidance for improving the fatigue performance of AM metals.

cond-mat.mtrl-sci↗

arXiv2019

Joint Embedding Learning of Educational Knowledge Graphs

As an efficient model for knowledge organization, the knowledge graph has been widely adopted in several fields, e.g., biomedicine, sociology, and education. And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks. In general, knowledge graph embedding techniques aim to learn vectorized representations which preserve the structural information of the graph. And conventional embedding learning models rely on structural relationships among entities and relations. However, in educational knowledge graphs, structural relationships are not the focus. Instead, rich literals of the graphs are more valuable. In this paper, we focus on this problem and propose a novel model for embedding learning of educational knowledge graphs. Our model considers both structural and literal information and jointly learns embedding representations. Three experimental graphs were constructed based on an educational knowledge graph which has been applied in real-world teaching. We conducted two experiments on the three graphs and other common benchmark graphs. The experimental results proved the effectiveness of our model and its superiority over other baselines when processing educational knowledge graphs.

cs.CL↗